AI in manufacturing

The main barriers are no longer a lack of AI ideas. They are the enterprise conditions required to move AI reliably into production. Legacy IT and OT environments, fragmented data, unclear measures of value, organizational silos, talent constraints and governance requirements can all slow the shift from isolated initiatives to enterprise capability.

Legacy systems can limit AI at scale

Manufacturing platforms were often designed for stable, localized operations rather than real-time intelligence and adaptive decision-making. Modernization therefore needs to balance integration and change with uptime, safety and operational continuity.

CGI's research finds that 51% of manufacturers identify legacy systems as a barrier to success.

Manufacturers are data-rich, but can still be insight-poor

Manufacturers generate significant volumes of operational data. The challenge is making that data accessible, contextualized, governed and trusted across plants, systems and functions. Only 48% of manufacturers say they can readily locate their data assets, restricting the information flows AI needs to operate effectively across an enterprise and its ecosystem.

AI investment needs a clearer connection to value

As AI activity expands, manufacturers need to prioritize use cases according to the operational and financial outcomes they can improve. Nearly half of manufacturers still aren't quantifying the results of their AI implementations, making clear ownership and agreed measures of success increasingly important.

AI changes operating models as well as technology

AI crosses traditional business, IT and OT boundaries. Progress can slow when decision rights are unclear, or data, technology and operations are managed separately. AI should not simply automate existing complexity. In many cases, value comes from reimagining and reengineering the workflow first, then embedding AI where it improves speed, quality, resilience or cost.

AI requires new skills and ways of working

Scaling AI is as much a people challenge as a technology one. Manufacturers need to build confidence in new AI-enabled ways of working, while addressing digital talent shortages and the retirement of experienced workers, whose operational knowledge is difficult to replace.

Trust and governance enable AI to scale

Manufacturers need confidence that AI-enabled decisions can be understood, governed and controlled.

Clear decision rights, trusted data, security, responsible AI practices and appropriate human oversight create the guardrails required to move AI safely from experimentation into production.